Net Buyers, Net Sellers, and Agricultural Landowner Support for Agricultural Zoning
Bibliographic record
Abstract
Agricultural zoning and land use restrictions are long-standing approaches for controlling non-agricultural development. Agricultural landowners may contest agricultural zoning if they expect zoning to reduce land prices on restricted land. However, it is common to find agricultural landowners on both sides of this issue. A prevailing economic explanation for variation in landowner support is that the price effect of zoning varies across land parcels and therefore, zoning may increases the value of some lands zoned for agricultural use. In this paper, we provide an additional explanation for variation in agricultural landowner support. We use the concepts of net buyers and net sellers of land to suggest that the utility effect of changing land prices depends on an agricultural landowner's position in the agricultural land market. Hence, even in situations where all agricultural landowners expect zoning to reduce agricultural land prices, some subset of agricultural landowners - i.e., net buyers - may benefit. Survey data from agricultural landowners is used to model the probability that an agricultural landowner will support agricultural zoning. The empirical findings are consistent with our hypothesis that net buyers and net sellers of agricultural land will differ in their support for agricultural zoning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".